Triple

T7606909
Position Surface form Disambiguated ID Type / Status
Subject Toyooka E180128 entity
Predicate mergedWith P77 FINISHED
Object Tanto
Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
 E676807 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tanto | Statement: [Toyooka, mergedWith, Tanto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanto
Context triple: [Toyooka, mergedWith, Tanto]
  • A. Toma
    Toma is a major Mande language spoken primarily in Guinea and neighboring West African countries.
  • B. Tyto
    Tyto is a genus of medium-sized owls best known for including the widespread barn owl and its close relatives.
  • C. Antu
    Antu is one of the four 8.2-meter Unit Telescopes of the Very Large Telescope array operated by the European Southern Observatory at Paranal in Chile.
  • D. Antu
    Antu is a Mesopotamian sky and mother goddess, best known as the consort of the supreme god Anu in ancient Sumerian and Akkadian religion.
  • E. Tibás
    Tibás is an urban canton in Costa Rica known for being part of the Greater San José metropolitan area and home to the popular football club Deportivo Saprissa.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tanto
Triple: [Toyooka, mergedWith, Tanto]
Generated description
Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanto
Target entity description: Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
  • A. Toma
    Toma is a major Mande language spoken primarily in Guinea and neighboring West African countries.
  • B. Tyto
    Tyto is a genus of medium-sized owls best known for including the widespread barn owl and its close relatives.
  • C. Antu
    Antu is one of the four 8.2-meter Unit Telescopes of the Very Large Telescope array operated by the European Southern Observatory at Paranal in Chile.
  • D. Antu
    Antu is a Mesopotamian sky and mother goddess, best known as the consort of the supreme god Anu in ancient Sumerian and Akkadian religion.
  • E. Tibás
    Tibás is an urban canton in Costa Rica known for being part of the Greater San José metropolitan area and home to the popular football club Deportivo Saprissa.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c69f3567008190ab01d2ca7b53584a completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9fe10408190b1c12bb8f911cea8 completed March 27, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86857db14819086d5ebd825d30e77 completed March 28, 2026, 11:46 p.m.
NEDg Description generation batch_69c86a12e1f08190ab214f4e95e986db completed March 28, 2026, 11:53 p.m.
NED2 Entity disambiguation (via description) batch_69c86a5b6f188190aafbf2e9fcb8b972 completed March 28, 2026, 11:55 p.m.
Created at: March 27, 2026, 3:54 p.m.